基于Gamma小波模型的网络流量预测

网络流量的精确预测是实现动态流量管理及控制的前提，由此提出一种基于Gamma小波模型的预测方法。将原始数据分解为高频信号和低频信号，采用Gamma小波模型对低频信号进行建模并获取服从Gamma分布的序列，分别对刚获取的序列以及高频信号采用加权一阶局域法进行预测，重构小波以合成数据。通过实验和数学分析的方法，证实该预测模型能够进行网络流量的短期预测。

Network Traffic Prediction Based on Gamma Wavelet Model

Dynamic traffic management and control are based on the accurate prediction of network traffic.This paper proposes a prediction method based on Gamma wavelet model.Original data series are decomposed by Haar wavelet into a low frequency signal and several high frequency signals.It models the low frequency signal and obtains the new series which obey Gamma distribution.It predicts the new series and the high frequency signals based on the weighted first order local prediction.The respective prediction series are synthesized to get the final prediction data.Experimental results and analytic studies show that the model does perform well in the short-term network traffic prediction.